diff --git a/python/cudf/cudf/core/groupby/groupby.py b/python/cudf/cudf/core/groupby/groupby.py
index 264f0ea5df6..a1a4596ba45 100644
--- a/python/cudf/cudf/core/groupby/groupby.py
+++ b/python/cudf/cudf/core/groupby/groupby.py
@@ -942,64 +942,10 @@ def corr(self, method="pearson", min_periods=1):
                 "Only pearson correlation is currently supported"
             )
 
-        # create expanded dataframe consisting all combinations of the
-        # struct columns-pairs to be correlated
-        # i.e (('col1', 'col1'), ('col1', 'col2'), ('col2', 'col2'))
-        _cols = self.grouping.values._data.to_pandas_index().tolist()
-        len_cols = len(_cols)
-
-        new_df_data = {}
-        for x, y in itertools.combinations_with_replacement(_cols, 2):
-            new_df_data[(x, y)] = cudf.DataFrame._from_data(
-                {"x": self.obj._data[x], "y": self.obj._data[y]}
-            ).to_struct()
-        new_gb = cudf.DataFrame._from_data(new_df_data).groupby(
-            by=self.grouping.keys
-        )
-
-        try:
-            gb_corr = new_gb.agg(lambda x: x.corr(method, min_periods))
-        except RuntimeError as e:
-            if "Unsupported groupby reduction type-agg combination" in str(e):
-                raise TypeError(
-                    "Correlation accepts only numerical column-pairs"
-                )
-            raise
-
-        # ensure that column-pair labels are arranged in ascending order
-        cols_list = [
-            (y, x) if i > j else (x, y)
-            for j, y in enumerate(_cols)
-            for i, x in enumerate(_cols)
-        ]
-        cols_split = [
-            cols_list[i : i + len_cols]
-            for i in range(0, len(cols_list), len_cols)
-        ]
-
-        # interleave: combine the correlation results for each column-pair
-        # into a single column
-        res = cudf.DataFrame._from_data(
-            {
-                x: gb_corr.loc[:, i].interleave_columns()
-                for i, x in zip(cols_split, _cols)
-            }
+        return self._cov_or_corr(
+            lambda x: x.corr(method, min_periods), "Correlation"
         )
 
-        # create a multiindex for the groupby correlated dataframe,
-        # to match pandas behavior
-        unsorted_idx = gb_corr.index.repeat(len_cols)
-        idx_sort_order = unsorted_idx._get_sorted_inds()
-        sorted_idx = unsorted_idx._gather(idx_sort_order)
-        if len(gb_corr):
-            # TO-DO: Should the operation below be done on the CPU instead?
-            sorted_idx._data[None] = as_column(
-                cudf.Series(_cols).tile(len(gb_corr.index))
-            )
-        res.index = MultiIndex._from_data(sorted_idx._data)
-
-        return res
-
     def cov(self, min_periods=0, ddof=1):
         """
         Compute the pairwise covariance among the columns of a DataFrame,
@@ -1085,8 +1031,18 @@ def cov(self, min_periods=0, ddof=1):
            val3  3.833333  12.333333  12.333333
         """
 
+        return self._cov_or_corr(
+            lambda x: x.cov(min_periods, ddof), "Covariance"
+        )
+
+    def _cov_or_corr(self, func, method_name):
+        """
+        internal function that is called by either corr() or cov()
+        for sort groupby correlation and covariance computations,
+        respectively.
+        """
         # create expanded dataframe consisting all combinations of the
-        # struct columns-pairs used in the covariance calculation
+        # struct columns-pairs to be used in the correlation or covariance
         # i.e. (('col1', 'col1'), ('col1', 'col2'), ('col2', 'col2'))
         column_names = self.grouping.values._column_names
         num_cols = len(column_names)
@@ -1122,13 +1078,11 @@ def cov(self, min_periods=0, ddof=1):
         ).groupby(by=self.grouping.keys)
 
         try:
-            gb_cov = column_pair_groupby.agg(
-                lambda x: x.cov(min_periods, ddof)
-            )
+            gb_cov_corr = column_pair_groupby.agg(func)
         except RuntimeError as e:
             if "Unsupported groupby reduction type-agg combination" in str(e):
                 raise TypeError(
-                    "Covariance accepts only numerical column-pairs"
+                    f"{method_name} accepts only numerical column-pairs"
                 )
             raise
 
@@ -1143,29 +1097,29 @@ def cov(self, min_periods=0, ddof=1):
             for i in range(0, len(cols_list), num_cols)
         ]
 
-        def combine_columns(gb_cov, ys):
-            list_of_columns = [gb_cov._data[y] for y in ys]
+        def combine_columns(gb_cov_corr, ys):
+            list_of_columns = [gb_cov_corr._data[y] for y in ys]
             frame = cudf.core.frame.Frame._from_columns(list_of_columns, ys)
             return interleave_columns(frame)
 
-        # interleave: combine the correlation results for each column-pair
-        # into a single column
+        # interleave: combines the correlation or covariance results for each
+        # column-pair into a single column
         res = cudf.DataFrame._from_data(
             {
-                x: combine_columns(gb_cov, ys)
+                x: combine_columns(gb_cov_corr, ys)
                 for ys, x in zip(cols_split, column_names)
             }
         )
 
-        # create a multiindex for the groupby correlated dataframe,
-        # to match pandas behavior
-        unsorted_idx = gb_cov.index.repeat(num_cols)
+        # create a multiindex for the groupby covariance or correlation
+        # dataframe, to match pandas behavior
+        unsorted_idx = gb_cov_corr.index.repeat(num_cols)
         idx_sort_order = unsorted_idx._get_sorted_inds()
         sorted_idx = unsorted_idx._gather(idx_sort_order)
-        if len(gb_cov):
+        if len(gb_cov_corr):
             # TO-DO: Should the operation below be done on the CPU instead?
             sorted_idx._data[None] = as_column(
-                np.tile(column_names, len(gb_cov.index))
+                np.tile(column_names, len(gb_cov_corr.index))
             )
         res.index = MultiIndex._from_data(sorted_idx._data)